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    A modified two-stage approach for joint modelling of longitudinal and time-to-event data

    Access Status
    Fulltext not available
    Authors
    Pham, Thi Thu Huong
    Nur, Darfiana
    Hoa, Pham
    Branford, Alan
    Date
    2018
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Pham, T.T.H. and Nur, D. and Hoa, P. and Branford, A. 2018. A modified two-stage approach for joint modelling of longitudinal and time-to-event data. Journal of Statistical Computation and Simulation. 88 (17): pp. 3379-3398.
    Source Title
    Journal of Statistical Computation and Simulation
    DOI
    10.1080/00949655.2018.1518449
    ISSN
    0094-9655
    Faculty
    Faculty of Science and Engineering
    School
    School of Elec Eng, Comp and Math Sci (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/79607
    Collection
    • Curtin Research Publications
    Abstract

    Joint models for longitudinal and time-to-event data have been applied in many different fields of statistics and clinical studies. However, the main difficulty these models have to face with is the computational problem. The requirement for numerical integration becomes severe when the dimension of random effects increases. In this paper, a modified two-stage approach has been proposed to estimate the parameters in joint models. In particular, in the first stage, the linear mixed-effects models and best linear unbiased predictorsare applied to estimate parameters in the longitudinal submodel. In the second stage, an approximation of the fully joint log-likelihood is proposed using the estimated the values of these parameters from the longitudinal submodel. Survival parameters are estimated bymaximizing the approximation of the fully joint loglikelihood. Simulation studies show that the approach performs well, especially when the dimension of random effects increases. Finally, we implement this approach on AIDS data.

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